// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.

#ifndef EIGEN_CXX11_TENSOR_TENSOR_CONCATENATION_H
#define EIGEN_CXX11_TENSOR_TENSOR_CONCATENATION_H

namespace Eigen {

/** \class TensorConcatenationOp
  * \ingroup CXX11_Tensor_Module
  *
  * \brief Tensor concatenation class.
  *
  *
  */
namespace internal {
    template <typename Axis, typename LhsXprType, typename RhsXprType> struct traits<TensorConcatenationOp<Axis, LhsXprType, RhsXprType>>
    {
        // Type promotion to handle the case where the types of the lhs and the rhs are different.
        typedef typename promote_storage_type<typename LhsXprType::Scalar, typename RhsXprType::Scalar>::ret Scalar;
        typedef typename promote_storage_type<typename traits<LhsXprType>::StorageKind, typename traits<RhsXprType>::StorageKind>::ret StorageKind;
        typedef typename promote_index_type<typename traits<LhsXprType>::Index, typename traits<RhsXprType>::Index>::type Index;
        typedef typename LhsXprType::Nested LhsNested;
        typedef typename RhsXprType::Nested RhsNested;
        typedef typename remove_reference<LhsNested>::type _LhsNested;
        typedef typename remove_reference<RhsNested>::type _RhsNested;
        static const int NumDimensions = traits<LhsXprType>::NumDimensions;
        static const int Layout = traits<LhsXprType>::Layout;
        enum
        {
            Flags = 0
        };
        typedef typename conditional<Pointer_type_promotion<typename LhsXprType::Scalar, Scalar>::val,
                                     typename traits<LhsXprType>::PointerType,
                                     typename traits<RhsXprType>::PointerType>::type PointerType;
    };

    template <typename Axis, typename LhsXprType, typename RhsXprType> struct eval<TensorConcatenationOp<Axis, LhsXprType, RhsXprType>, Eigen::Dense>
    {
        typedef const TensorConcatenationOp<Axis, LhsXprType, RhsXprType>& type;
    };

    template <typename Axis, typename LhsXprType, typename RhsXprType>
    struct nested<TensorConcatenationOp<Axis, LhsXprType, RhsXprType>, 1, typename eval<TensorConcatenationOp<Axis, LhsXprType, RhsXprType>>::type>
    {
        typedef TensorConcatenationOp<Axis, LhsXprType, RhsXprType> type;
    };

}  // end namespace internal

template <typename Axis, typename LhsXprType, typename RhsXprType>
class TensorConcatenationOp : public TensorBase<TensorConcatenationOp<Axis, LhsXprType, RhsXprType>, WriteAccessors>
{
public:
    typedef TensorBase<TensorConcatenationOp<Axis, LhsXprType, RhsXprType>, WriteAccessors> Base;
    typedef typename internal::traits<TensorConcatenationOp>::Scalar Scalar;
    typedef typename internal::traits<TensorConcatenationOp>::StorageKind StorageKind;
    typedef typename internal::traits<TensorConcatenationOp>::Index Index;
    typedef typename internal::nested<TensorConcatenationOp>::type Nested;
    typedef typename internal::promote_storage_type<typename LhsXprType::CoeffReturnType, typename RhsXprType::CoeffReturnType>::ret CoeffReturnType;
    typedef typename NumTraits<Scalar>::Real RealScalar;

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorConcatenationOp(const LhsXprType& lhs, const RhsXprType& rhs, Axis axis)
        : m_lhs_xpr(lhs), m_rhs_xpr(rhs), m_axis(axis)
    {
    }

    EIGEN_DEVICE_FUNC
    const typename internal::remove_all<typename LhsXprType::Nested>::type& lhsExpression() const { return m_lhs_xpr; }

    EIGEN_DEVICE_FUNC
    const typename internal::remove_all<typename RhsXprType::Nested>::type& rhsExpression() const { return m_rhs_xpr; }

    EIGEN_DEVICE_FUNC const Axis& axis() const { return m_axis; }

    EIGEN_TENSOR_INHERIT_ASSIGNMENT_OPERATORS(TensorConcatenationOp)
protected:
    typename LhsXprType::Nested m_lhs_xpr;
    typename RhsXprType::Nested m_rhs_xpr;
    const Axis m_axis;
};

// Eval as rvalue
template <typename Axis, typename LeftArgType, typename RightArgType, typename Device>
struct TensorEvaluator<const TensorConcatenationOp<Axis, LeftArgType, RightArgType>, Device>
{
    typedef TensorConcatenationOp<Axis, LeftArgType, RightArgType> XprType;
    typedef typename XprType::Index Index;
    static const int NumDims = internal::array_size<typename TensorEvaluator<LeftArgType, Device>::Dimensions>::value;
    static const int RightNumDims = internal::array_size<typename TensorEvaluator<RightArgType, Device>::Dimensions>::value;
    typedef DSizes<Index, NumDims> Dimensions;
    typedef typename XprType::Scalar Scalar;
    typedef typename XprType::CoeffReturnType CoeffReturnType;
    typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
    typedef StorageMemory<CoeffReturnType, Device> Storage;
    typedef typename Storage::Type EvaluatorPointerType;
    enum
    {
        IsAligned = false,
        PacketAccess = TensorEvaluator<LeftArgType, Device>::PacketAccess && TensorEvaluator<RightArgType, Device>::PacketAccess,
        BlockAccess = false,
        PreferBlockAccess = TensorEvaluator<LeftArgType, Device>::PreferBlockAccess || TensorEvaluator<RightArgType, Device>::PreferBlockAccess,
        Layout = TensorEvaluator<LeftArgType, Device>::Layout,
        RawAccess = false
    };

    //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
    typedef internal::TensorBlockNotImplemented TensorBlock;
    //===--------------------------------------------------------------------===//

    EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
        : m_leftImpl(op.lhsExpression(), device), m_rightImpl(op.rhsExpression(), device), m_axis(op.axis())
    {
        EIGEN_STATIC_ASSERT(
            (static_cast<int>(TensorEvaluator<LeftArgType, Device>::Layout) == static_cast<int>(TensorEvaluator<RightArgType, Device>::Layout) || NumDims == 1),
            YOU_MADE_A_PROGRAMMING_MISTAKE);
        EIGEN_STATIC_ASSERT((NumDims == RightNumDims), YOU_MADE_A_PROGRAMMING_MISTAKE);
        EIGEN_STATIC_ASSERT((NumDims > 0), YOU_MADE_A_PROGRAMMING_MISTAKE);

        eigen_assert(0 <= m_axis && m_axis < NumDims);
        const Dimensions& lhs_dims = m_leftImpl.dimensions();
        const Dimensions& rhs_dims = m_rightImpl.dimensions();
        {
            int i = 0;
            for (; i < m_axis; ++i)
            {
                eigen_assert(lhs_dims[i] > 0);
                eigen_assert(lhs_dims[i] == rhs_dims[i]);
                m_dimensions[i] = lhs_dims[i];
            }
            eigen_assert(lhs_dims[i] > 0);  // Now i == m_axis.
            eigen_assert(rhs_dims[i] > 0);
            m_dimensions[i] = lhs_dims[i] + rhs_dims[i];
            for (++i; i < NumDims; ++i)
            {
                eigen_assert(lhs_dims[i] > 0);
                eigen_assert(lhs_dims[i] == rhs_dims[i]);
                m_dimensions[i] = lhs_dims[i];
            }
        }

        if (static_cast<int>(Layout) == static_cast<int>(ColMajor))
        {
            m_leftStrides[0] = 1;
            m_rightStrides[0] = 1;
            m_outputStrides[0] = 1;

            for (int j = 1; j < NumDims; ++j)
            {
                m_leftStrides[j] = m_leftStrides[j - 1] * lhs_dims[j - 1];
                m_rightStrides[j] = m_rightStrides[j - 1] * rhs_dims[j - 1];
                m_outputStrides[j] = m_outputStrides[j - 1] * m_dimensions[j - 1];
            }
        }
        else
        {
            m_leftStrides[NumDims - 1] = 1;
            m_rightStrides[NumDims - 1] = 1;
            m_outputStrides[NumDims - 1] = 1;

            for (int j = NumDims - 2; j >= 0; --j)
            {
                m_leftStrides[j] = m_leftStrides[j + 1] * lhs_dims[j + 1];
                m_rightStrides[j] = m_rightStrides[j + 1] * rhs_dims[j + 1];
                m_outputStrides[j] = m_outputStrides[j + 1] * m_dimensions[j + 1];
            }
        }
    }

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }

    // TODO(phli): Add short-circuit memcpy evaluation if underlying data are linear?
    EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType)
    {
        m_leftImpl.evalSubExprsIfNeeded(NULL);
        m_rightImpl.evalSubExprsIfNeeded(NULL);
        return true;
    }

    EIGEN_STRONG_INLINE void cleanup()
    {
        m_leftImpl.cleanup();
        m_rightImpl.cleanup();
    }

    // TODO(phli): attempt to speed this up. The integer divisions and modulo are slow.
    // See CL/76180724 comments for more ideas.
    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
    {
        // Collect dimension-wise indices (subs).
        array<Index, NumDims> subs;
        if (static_cast<int>(Layout) == static_cast<int>(ColMajor))
        {
            for (int i = NumDims - 1; i > 0; --i)
            {
                subs[i] = index / m_outputStrides[i];
                index -= subs[i] * m_outputStrides[i];
            }
            subs[0] = index;
        }
        else
        {
            for (int i = 0; i < NumDims - 1; ++i)
            {
                subs[i] = index / m_outputStrides[i];
                index -= subs[i] * m_outputStrides[i];
            }
            subs[NumDims - 1] = index;
        }

        const Dimensions& left_dims = m_leftImpl.dimensions();
        if (subs[m_axis] < left_dims[m_axis])
        {
            Index left_index;
            if (static_cast<int>(Layout) == static_cast<int>(ColMajor))
            {
                left_index = subs[0];
                EIGEN_UNROLL_LOOP
                for (int i = 1; i < NumDims; ++i) { left_index += (subs[i] % left_dims[i]) * m_leftStrides[i]; }
            }
            else
            {
                left_index = subs[NumDims - 1];
                EIGEN_UNROLL_LOOP
                for (int i = NumDims - 2; i >= 0; --i) { left_index += (subs[i] % left_dims[i]) * m_leftStrides[i]; }
            }
            return m_leftImpl.coeff(left_index);
        }
        else
        {
            subs[m_axis] -= left_dims[m_axis];
            const Dimensions& right_dims = m_rightImpl.dimensions();
            Index right_index;
            if (static_cast<int>(Layout) == static_cast<int>(ColMajor))
            {
                right_index = subs[0];
                EIGEN_UNROLL_LOOP
                for (int i = 1; i < NumDims; ++i) { right_index += (subs[i] % right_dims[i]) * m_rightStrides[i]; }
            }
            else
            {
                right_index = subs[NumDims - 1];
                EIGEN_UNROLL_LOOP
                for (int i = NumDims - 2; i >= 0; --i) { right_index += (subs[i] % right_dims[i]) * m_rightStrides[i]; }
            }
            return m_rightImpl.coeff(right_index);
        }
    }

    // TODO(phli): Add a real vectorization.
    template <int LoadMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
    {
        const int packetSize = PacketType<CoeffReturnType, Device>::size;
        EIGEN_STATIC_ASSERT((packetSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
        eigen_assert(index + packetSize - 1 < dimensions().TotalSize());

        EIGEN_ALIGN_MAX CoeffReturnType values[packetSize];
        EIGEN_UNROLL_LOOP
        for (int i = 0; i < packetSize; ++i) { values[i] = coeff(index + i); }
        PacketReturnType rslt = internal::pload<PacketReturnType>(values);
        return rslt;
    }

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const
    {
        const double compute_cost = NumDims * (2 * TensorOpCost::AddCost<Index>() + 2 * TensorOpCost::MulCost<Index>() + TensorOpCost::DivCost<Index>() +
                                               TensorOpCost::ModCost<Index>());
        const double lhs_size = m_leftImpl.dimensions().TotalSize();
        const double rhs_size = m_rightImpl.dimensions().TotalSize();
        return (lhs_size / (lhs_size + rhs_size)) * m_leftImpl.costPerCoeff(vectorized) +
               (rhs_size / (lhs_size + rhs_size)) * m_rightImpl.costPerCoeff(vectorized) + TensorOpCost(0, 0, compute_cost);
    }

    EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; }

#ifdef EIGEN_USE_SYCL
    // binding placeholder accessors to a command group handler for SYCL
    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler& cgh) const
    {
        m_leftImpl.bind(cgh);
        m_rightImpl.bind(cgh);
    }
#endif

protected:
    Dimensions m_dimensions;
    array<Index, NumDims> m_outputStrides;
    array<Index, NumDims> m_leftStrides;
    array<Index, NumDims> m_rightStrides;
    TensorEvaluator<LeftArgType, Device> m_leftImpl;
    TensorEvaluator<RightArgType, Device> m_rightImpl;
    const Axis m_axis;
};

// Eval as lvalue
template <typename Axis, typename LeftArgType, typename RightArgType, typename Device>
struct TensorEvaluator<TensorConcatenationOp<Axis, LeftArgType, RightArgType>, Device>
    : public TensorEvaluator<const TensorConcatenationOp<Axis, LeftArgType, RightArgType>, Device>
{
    typedef TensorEvaluator<const TensorConcatenationOp<Axis, LeftArgType, RightArgType>, Device> Base;
    typedef TensorConcatenationOp<Axis, LeftArgType, RightArgType> XprType;
    typedef typename Base::Dimensions Dimensions;
    enum
    {
        IsAligned = false,
        PacketAccess = TensorEvaluator<LeftArgType, Device>::PacketAccess && TensorEvaluator<RightArgType, Device>::PacketAccess,
        BlockAccess = false,
        PreferBlockAccess = TensorEvaluator<LeftArgType, Device>::PreferBlockAccess || TensorEvaluator<RightArgType, Device>::PreferBlockAccess,
        Layout = TensorEvaluator<LeftArgType, Device>::Layout,
        RawAccess = false
    };

    //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
    typedef internal::TensorBlockNotImplemented TensorBlock;
    //===--------------------------------------------------------------------===//

    EIGEN_STRONG_INLINE TensorEvaluator(XprType& op, const Device& device) : Base(op, device)
    {
        EIGEN_STATIC_ASSERT((static_cast<int>(Layout) == static_cast<int>(ColMajor)), YOU_MADE_A_PROGRAMMING_MISTAKE);
    }

    typedef typename XprType::Index Index;
    typedef typename XprType::Scalar Scalar;
    typedef typename XprType::CoeffReturnType CoeffReturnType;
    typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(Index index)
    {
        // Collect dimension-wise indices (subs).
        array<Index, Base::NumDims> subs;
        for (int i = Base::NumDims - 1; i > 0; --i)
        {
            subs[i] = index / this->m_outputStrides[i];
            index -= subs[i] * this->m_outputStrides[i];
        }
        subs[0] = index;

        const Dimensions& left_dims = this->m_leftImpl.dimensions();
        if (subs[this->m_axis] < left_dims[this->m_axis])
        {
            Index left_index = subs[0];
            for (int i = 1; i < Base::NumDims; ++i) { left_index += (subs[i] % left_dims[i]) * this->m_leftStrides[i]; }
            return this->m_leftImpl.coeffRef(left_index);
        }
        else
        {
            subs[this->m_axis] -= left_dims[this->m_axis];
            const Dimensions& right_dims = this->m_rightImpl.dimensions();
            Index right_index = subs[0];
            for (int i = 1; i < Base::NumDims; ++i) { right_index += (subs[i] % right_dims[i]) * this->m_rightStrides[i]; }
            return this->m_rightImpl.coeffRef(right_index);
        }
    }

    template <int StoreMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writePacket(Index index, const PacketReturnType& x)
    {
        const int packetSize = PacketType<CoeffReturnType, Device>::size;
        EIGEN_STATIC_ASSERT((packetSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
        eigen_assert(index + packetSize - 1 < this->dimensions().TotalSize());

        EIGEN_ALIGN_MAX CoeffReturnType values[packetSize];
        internal::pstore<CoeffReturnType, PacketReturnType>(values, x);
        for (int i = 0; i < packetSize; ++i) { coeffRef(index + i) = values[i]; }
    }
};

}  // end namespace Eigen

#endif  // EIGEN_CXX11_TENSOR_TENSOR_CONCATENATION_H
